package com.atguigu.java.langchain4j.config;

import com.alibaba.dashscope.common.Message;
import com.atguigu.java.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.ChatMemory;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.data.mongodb.core.MongoTemplate;

import java.util.Arrays;
import java.util.List;

/**
 * @author 沈复龙
 * @date 2025/6/6
 * @description
 */
@Configuration
public class XiaozhiAgentConfig {

    @Autowired
    private EmbeddingModel embeddingModel;
    @Autowired
    private EmbeddingStore embeddingStore;

    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;

    @Bean
    public ChatMemoryProvider chatMemoryProviderXiaozhi(){
       return memoryId ->
           MessageWindowChatMemory.builder().id(memoryId)
                   .maxMessages(20)
                   .chatMemoryStore(mongoChatMemoryStore)
                   .build();

    }

    @Bean("contentRetrieverXiaozhi")
    public  ContentRetriever contentRetrievert(){
        Document document1 = FileSystemDocumentLoader.loadDocument("C:\\baidunetdiskdownload\\knowledge\\knowledge\\医院信息.md");
        Document document2 = FileSystemDocumentLoader.loadDocument("C:\\baidunetdiskdownload\\knowledge\\knowledge\\科室信息.md");
        Document document3 = FileSystemDocumentLoader.loadDocument("C:\\baidunetdiskdownload\\knowledge\\knowledge\\神经内科.md");
        List<Document> documents = Arrays.asList(document1, document2, document3);
        //使用向量内存
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }
    @Bean("contentRetrieverXiaozhiPincone")
    public ContentRetriever contentRetrieverXiaozhiPincone(){
        // 创建一个 EmbeddingStoreContentRetriever 对象，用于从嵌入存储中检索内容
        return EmbeddingStoreContentRetriever
                .builder()
                // 设置用于生成嵌入向量的嵌入模型
                .embeddingModel(embeddingModel)
                // 指定要使用的嵌入存储
                .embeddingStore(embeddingStore)
                // 设置最大检索结果数量，这里表示最多返回 1 条匹配结果
                .maxResults(1)
                // 设置最小得分阈值，只有得分大于等于 0.8 的结果才会被返回
                .minScore(0.8)
                // 构建最终的 EmbeddingStoreContentRetriever 实例
                .build();
    }
}
